Explore the vast range of titles available.

The kinetics and mechanism of methylene blue adsorption onto raw pine cone biomass ( Pinus radiata ) was investigated under various physicochemical parameters. The extent of the methylene blue dye adsorption increased with increases in initial dye concentration, contact time and solution pH but decreases with the amount of adsorbent, salt concentration and temperature of the system. Overall the kinetic studies showed that the methylene blue adsorption process followed pseudo-second-order kinetics among various kinetic models tested. The different kinetic parameters including rate constant, half-adsorption time and diffusion coefficient are determined at different physicochemical conditions. Equilibrium data were best represented by Langmuir isotherm among Langmuir and Freundlich adsorption isotherm. The maximum monolayer adsorption capacity of pine cone biomass was 109.89 mg/g at 30°C. The value of separation factor, R L , from Langmuir equation and Freundlich constant, n , both give an indication of favourable adsorption. Thermodynamic parameters such as standard Gibbs free energy (∆ G 0 ), standard enthalpy (∆ H 0 ), standard entropy (∆ S 0 ) and the activation energy ( A ) were calculated. A single-stage batch absorber design for the methylene blue adsorption onto pine cone biomass has been presented based on the Langmuir isotherm model equation.

The adsorption capacity of pine tree leaves for removal of methylene blue (MB) from aqueous solution was investigated in a batch system. The effects of the process variables, such as solution pH, contact time, initial dye concentration, amount of adsorbent, agitation speed, salt concentration, and system temperature on the adsorption process were studied. The extent of methylene blue dye adsorption increased with increase in initial dye concentration, contact time, agitation speed, temperature, and solution pH but decreased with increased in amount of adsorbent and salt concentration. Equilibrium data were best described by both Langmuir isotherm and Freundlich adsorption isotherm. The maximum monolayer adsorption capacity of pine tree leaves biomass was 126.58 mg/g at 30 °C. The value of separation factor, R L , from Langmuir equation and Freundlich constant, n , both give an indication of favorable adsorption. The intrapartical diffusion model, liquid film diffusion model, double exponential model, pseudo-first and second order model were used to describe the kinetic and mechanism of adsorption process. A single stage bath adsorber design for the MB adsorption onto pine tree leaves has been presented based on the Langmuir isotherm model equation. Thermodynamic parameters such as standard Gibbs free energy (Δ G 0 ), standard enthalpy (Δ H 0 ), and standard entropy (Δ S 0 ) were calculated.

Two Co oxide catalysts supported on natural zeolites from Indonesia (INZ) and Australia (ANZ) were prepared and used to activate peroxymonosulphate for degradation of aqueous phenol. The two catalysts were characterized by several techniques such as X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy (EDS) and N 2 adsorption. It was found that Co/INZ and Co/ANZ are effective in activation of peroxymonosulphate to produce sulphate radicals for phenol degradation. Co/INZ and Co/ANZ could remove phenol up to 100 and 70 %, respectively, at the conditions of 25 ppm phenol (500 mL), 0.2 g catalyst, 1 g oxone and 25 °C. Several parameters such as amount of catalyst loading, phenol concentration, oxidant concentration and temperature were found to be the key factors influencing phenol degradation. A pseudo first order would fit to phenol degradation kinetics, and the activation energies on Co/INZ and Co/ANZ were obtained as 52.4 and 61.3 kJ/mol, respectively.

In the realm of conventional affordance detection, the primary objective is to provide insights into the potential uses of objects. However, a significant limitation remains as these conventional methods merely treat affordance detection as a semantic segmentation task, disregarding the crucial aspect of interpreting affordances for actions that can be performed by manipulator. To address this critical gap, we present a novel pipeline incorporating the Intelligent Action Library (IAL) concept. This framework enables affordance interpretation for various manipulation tasks, allowing robots to be taught and guided on how to execute specific actions based on the detected affordances and human-robot interaction. Through real-world experiments, we have demonstrated the ingenuity and dependability of our pipeline, effectively bridging the gap between affordance detection and manipulation task planning and execution. The integration of IAL facilitates a seamless connection between understanding affordances and empowering robots to perform tasks with precision and efficiency. The demo link is available to the public: https://youtu.be/_oBAer2Vl8k

The authors propose in this study a new adaptive discriminative metric learning method for facial expression recognition. Although a number of methods have been proposed for facial expression recognition, most of them apply the conventional Euclidean distance metric to measure the similarity/dissimilarity of face expression images and cannot effectively characterise such similarity/dissimilarity of these images because the intrinsic space of face images usually do not lie in such an Euclidean space. Motivated by the fact that between-class facial images with small differences are more easily mis-classified than those with large differences, the authors propose learning an adaptive metric by imposing large penalties on between-class samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative information can be extracted in the learned distance metric for facial expression recognition. Experimental results on three widely used face datasets are presented to demonstrate the efficacy of the proposed method. [PUBLICATION ABSTRACT]

Sludge dewatering is important in sludge management and disposal. In practice, chemical conditioners are often introduced to aid sludge dewatering. This study investigated the simultaneous application of chitosan and metal cations as dual-conditioners to improve sludge dewaterability. The dewatering performance of sludge was evaluated using three common measurements, i.e. capillary suction time, specific resistance to filtration, and moisture content of the filtered sludge cake. The effectiveness of metal cations in sludge conditioning and dewatering was found, in ascending order, to be Na+ < K+ ≈ Mg2+ < Ca2+ < Al3+ < Fe3+. Dual-conditioning using chitosan and metal cations further enhanced dewaterability. Cations may have significant effects on sludge conditioning by neutralization of negative surface charges, bridging of floc components, and the salting out effect, leading to improved dewaterability when used in conjunction with chitosan.

Automating the welding process for the shipbuilding industry is very challenging and important, as this industry relies heavily on quality welds. Conventional robotic welding systems are seldom used because the welding tasks in shipyards are characterized by non-standardized workpieces which are large but small in batch sizes. Furthermore, geometries and locations of the workpieces are uncertain. To tackle the problem, a Ship Welding Robot System (SWERS) has been developed for the welding process. The main features of the SWERS include a special teaching procedure that allows the human user to teach the robot welding paths at a much easier and faster pace. In addition, operation of the system is made easier through a custom designed man-machine interface. Through this interface, only a few buttons need to be pressed to command the robot into different modes. Optimized welding parameters can be selected from a large database through a Graphical User Interface system.

Robots have been traditionally used as positioning devices without muchregard to external forces experienced by the tool. This has limited furtherpotential applications of robots in automation. Most tasks that remain to beautomated require constrained robot motion and/or involve work done by therobot on the environment. Such tasks require both force and positioncontrol. The ability to control the end-effector compliance is critical tosuccessful force and position control tasks. Although the end-effectorcompliance can be actively controlled through the joint flexibilitiesprovided by the joint servos or by active force sensing, the usefulness ofhaving the minimum passive compliance in addition to active compliancecontrol can improve performance. In surface following, for example, it isnecessary to make the end-point of a robot have the right compliance toprevent jamming. The usefulness of passive compliance has been demonstratedby the use of compliance-devices on the robot end-effector such as theRemote Center Compliance. The natural compliance inherent in light weightand flexible robot structures, however, can be exploited to provide thenecessary passive compliance required.In this paper we present a novel framework for computing the end-effectorcompliance from the compliance offered by the limbs of a serial robot. Theemphasis is on the explanation of the passive end-effector complianceresulting from these structures, and particular attention is given to theuse of these results in the selection of the type of robot for a particulartask. We show examples of end-effector compliances as functions of jointconfigurations for the SCARA- and PUMA-type robots. The joint-configurationdependent end-effector compliance can be used to select the desired robotpose for the performance of a robotic task.

Conventional mobile robots rely on pre-built point cloud maps for online localization. These map points are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the architectural, engineering and construction (AEC) industry, asplanned building information modelings (BIM) are available for management and operation. In this paper, we consider the use of the digital representations of BIM for robot localization in built environments. First, we convert BIM data into localization-oriented point clouds, which is easy to implement and operate compared to relatively complex SLAM systems. Then, we perform iterative closest point (ICP)-based localization on the metric map using a laser scanner. The experiments are tested using collected laser data and BIM in the real world. The results show that ICP-based localization can track the robot pose with low errors (< [0.20m, 2.50°]), thus demonstrating the feasibility of BIM-based robot localization. In addition, we also discuss the reasons for errors, including the deviations between as-planned BIM and asbuilt status.

To get the best features of both deliberative and reactive controllers, present mobile robot control architectures are designed to accommodate both types of controller. However, these architectures are still very rigidly structured thus deliberative modules are always assigned to the same role as a high-level planner or sequencer while low-level reactive modules are still the ones directly interacting with the robot environment. Furthermore, within these architectures communication and interface between modules are if not strongly established, they are very complex thus making them unsuitable for simple robotic systems. Our idea in this paper is to present a control architecture that is flexible in the sense that it can easily integrate both reactive and deliberative modules but not necessarily restricting the role of each type of controller. Communication between modules is through simple arbitration schemes while interface is by connecting a common communication line between modules and simple read and/or write access of data objects. On top of these features, the proposed control architecture is scalable and exhibits graceful degradation when some of the modules fail, similar to the present mobile robot architectures. Our idea has enabled our four-legged robot to walk autonomously in a structured uneven terrain.